Linearized Bregman Iterations for Frame-Based Image Deblurring

Top Cited Papers
Publisher Website
Google Scholar
Abstract
Real images usually have sparse approximations under some tight frame systems derived from framelets, an oversampled discrete (window) cosine, or a Fourier transform. In this paper, we propose a method for image deblurring in tight frame domains. It is reduced to finding a sparse solution of a system of linear equations whose coefficient matrix is rectangular. Then, a modified version of the linearized Bregman iteration proposed and analyzed in [J.-F. Cai, S. Osher, and Z. Shen, Math. Comp., to appear, UCLA CAM Report (08-52), 2008; J.-F. Cai, S. Osher, and Z. Shen, Math. Comp., to appear, UCLA CAM Report (08-06), 2008; S. Osher et al., UCLA CAM Report (08-37), 2008; W. Yin et al., SIAM J. Imaging Sci., 1 (2008), pp. 143-168] can be applied. Numerical examples show that the method is very simple to implement, robust to noise, and effective for image deblurring.

This publication has 29 references indexed in Scilit: